Details Of Components Research Articles

HRU-TNet: Hybrid Residual U-Transformer Network for diabetic retinopathy multi-lesion segmentation

The automatic segmentation of diabetic retinopathy (DR) holds significant importance for assisting physicians in diagnosis and treatment. Given the complexity, high inter-class similarity, and uncertainty of DR, it is crucial to integrate multiscale information between lesions and establish global correlations among them. To address these issues, a novel HRU-TNet (Hybrid Residual U-Transformer Network) algorithm for retinal lesion segmentation is proposed. In this framework, the network is augmented with lightweight self-attention residual U-modules (LSA-RSU) to capture high-frequency details of the lesions and global contextual information. The skip connections are then enhanced through interactive residual transformer fusion modules (IRTF) and channel-cross attention (CCA), promoting dependencies among features at different scales and filtering out interfering information to guide feature fusion and eliminate ambiguity. Additionally, a novel retinal image enhancement technique is devised, employing local wavelet transformations to capture detailed components of the retinal images, thereby enhancing the representational capacity of the segmentation network. Data augmentation is also performed to ensure network adaptability to small datasets. Comprehensive experiments conducted on the publicly available IDRID and e_ophtha datasets yielded average AUC_PR values of 0.709 and 0.451, respectively. The proposed approach demonstrated superior generalization on the DDR dataset compared to other methods mentioned in the literature. These results demonstrate that our proposed method is better suited for small retinal datasets, exhibiting improved segmentation accuracy and generalization compared to existing approaches.

A Hybrid Algorithm Combining Data-Driven and Simulation-Based Reinforcement Learning Approaches to Energy Management of Hybrid Electric Vehicles

Energy management strategy (EMS) is one of the key technologies that improves the fuel efficiency of hybrid electric vehicles (HEVs) by governing the energy flow between the fuel tank and the electric energy storage. With the rapid development of artificial intelligence especially after the great success of AlphaGo, reinforcement learning (RL) has opened up a new window for EMS. Although many RL-based solutions have been successfully applied to EMS tasks, most current RL-based approaches only consider RL as an offline optimization tool, i.e., RL is used to solve optimization problems given a simulation model. This method may be utilized to realize optimal control in simulation, but there is no guarantee that a well-behaved performance can also be achieved in the real-physical world due to the simulation-to-real gap. Meanwhile, for many industrial EMS tasks especially when optimizing an existing controller, a high-fidelity simulator may be unavailable, but some logging data generated by some suboptimal existing controller and simple models of the principal powertrain components neglecting detailed component dynamics could exist. In this context, a hybrid algorithm combining data-driven and simulation-based RL approach is proposed that tries to learn experience from both the real logging data and simulated simple model. Based on the hardware-in-the-loop (HIL) results, a near optimal policy can be obtained (fuel consumption is decreased by approximately 6.10%) by this hybrid algorithm from a small batch logging data and a simple simulated model when compared with the dynamic programming (DP) method using a high-fidelity simulation model mimicking the real-physical world.

Revealing the binding mechanism of BACE1 inhibitors through molecular dynamics simulations

Alzheimer’s disease is a debilitating neurodegenerative disorder, and the Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) is a key therapeutic target in its treatment. This study employs molecular dynamics simulations and binding energy analysis to investigate the binding interactions between BACE1 and four selected small molecules: CNP520, D9W, NB641, and NB360. The binding model analysis indicates that the binding of BACE1 with four molecules are stable, except the loop regions show significant fluctuation. The binding free energy analyses reveal that NB360 exhibits the highest binding affinity with BACE1, surpassing other molecules (CNP520, D9W, and NB641). Detailed energy component assessments highlight the critical roles of electrostatic interactions and van der Waals forces in the binding process. Furthermore, residue contribution analysis identifies key amino acids influencing the binding process across all systems. Hydrogen bond analysis reveals a limited number of bonds between BACE1 and each small molecule, highlighting the importance of structural modifications to enable more stable hydrogen bonds. This research provides valuable insights into the molecular mechanisms of potential Alzheimer’s disease therapeutics, guiding the way for improved drug design and the development of effective treatments targeting BACE1. Communicated by Ramaswamy H. Sarma

Network pharmacology and gut microbiota insights: unraveling Shenling Baizhu powder's role in psoriasis treatment.

Background: Psoriasis, a chronic skin condition characterized by systemic inflammation and altered gut microbiota, has been a target of Traditional Chinese Medicine (TCM) for centuries. Shenling Baizhu Powder (SLBZP), a TCM formulation, holds promise for treating inflammatory diseases, but its specific role in psoriasis and impact on gut microbiota is not fully understood. Objective: This study aims to elucidate the mechanism of SLBZP in treating psoriasis, integrating component analysis, network pharmacology, and experimental validation in mice models. Methods: We commenced with a detailed component analysis of SLBZP using liquid chromatograph and mass spectrometer (LC-MS). Network pharmacology analysis was used to predict the potential action targets and pathways of SLBZP in psoriasis. An in vivo experiment was conducted with psoriasis mice models, treated with SLBZP. Therapeutic effects were assessed via symptomatology, histopathology, and immunohistochemical analysis. Gut microbiota composition was analyzed using 16S rRNA gene sequencing. Results: A total of 42 main components and quality markers were identified, primarily from licorice and ginseng, including flavonoids, saponins and other markers. PPI topology analysis showed that TNF, IL-6, IL-1β, TP53 and JUN were the core DEPs. 168 signaling pathways including lipid and atherosclerosis, AGE-RAGE signaling pathway, IL-17 signaling pathway and Th17 cell differentiation were enriched by KEGG. SLBZP demonstrated significant therapeutic effects on psoriasis in mice, with alterations in skin pathology and biomarkers. Additionally, notable changes in gut microbiota composition were observed post-treatment, indicating a possible gut-skin axis involvement. Conclusion: This research has pinpointed lipid metabolism as a key pathway in the treatment of psoriasis with SLBZP. It explores how SLBZP's modulation of gut microbiota and lipid metabolism can alleviate psoriasis, suggesting that balancing gut microbiota may reduce inflammation mediators and offer therapeutic benefits. This underscores lipid metabolism modulation as a potential new strategy in psoriasis treatment.

The aspirational promise of soldiering: an analysis of military recruitment testimonials

ABSTRACT Young people across the globe willingly join the military, knowing that they may be required to kill, maim, and perhaps even die. Considering the starkness of this barebone condition, armed forces strive to make enlisting desirable through recruitment campaigns. While the appeal of military aspirational promises – particularly the promise of manhood – figure centrally in much critical scholarship, the detailed components of these pledges warrant closer scrutiny. This article therefore explores the aspirational promises pledged in military recruitment campaigns from the US Army and the Swedish Armed Forces. Based on a narrative analysis of video testimonials in which ‘real’ soldiers tell their enlistment stories, we lay bare the overarching story grammar made up of distinct plot points (lack, hardship, agency and growth) that comprise the aspirational promises in these campaigns. In tracing these plot points in two distinct sites, the article offers well-needed insight into how the appeal of contemporary soldiering is being constructed and how this appeal attempts to govern potential soldiers. Despite their differences, the campaigns present soldiering as a or the way to regulate and govern the self in relation to norms about what constitutes a successful, self-fulfilled or complete citizen-subject. The aspirational figures in the testimonials promise that one can redress one’s deficiencies and transcend the racialized, gendered, and classed, etc. limits one confronts in oneself and in civilian life. These soldier stories, despite their hyperreal climaxes and journeys, appear to be real and lived and theirs. Vitally, they could also be ours.

Bottom-preferred stripping mechanism towards quantified inactive metallic Zn0-dominant zinc loss in rechargeable zinc metal battery

Electrically isolated metallic Zn (Zn0) and electrochemically side reaction (ECSR), causes irreversible anode capacity loss and limited cycle life in zinc metal battery. However, the quantitative distinguishment between inactive metallic Zn0 and ECSR, their formation mechanism and correlation with anode cycling reversibility have never been disclosed, limiting further anode modification design. Here, we develop an acid-assisted continuous titration-collection-gas chromatography technique (AAC-TCGC) that accurately quantify the amount of inactive Zn0 and ECSR in anode, and discover that inactive Zn0 accounts for the majority of Zn loss, rather than the commonly-assumed electrochemically side reaction-derived ECSR-dominant Zn anode capacity loss. Detailed component of SEI (Solid Electrolyte Interface Layer) compound is also characterized, illustrating a solvent-derived zinc oxide/hydroxide-dominant SEI that facilitates vertical/inclined thin-plate Zn deposition, but salt-derived ZnF2-dominant SEI that favors horizontal platelet-shaped Zn(002) deposition. Simulation results reveal a new electric resistance-derived, bottom-preferred Zn stripping mechanism that leads to abundant inactive Zn0 formation in vertical thin-plate Zn deposition structure, but few inactive Zn0 in dumpy platelet-shaped Zn structure. Our study provides new theories and strategies on the distinguishment, formation mechanism and structure-performance relationship of inactive Zn, important for building more efficient Zn metal battery.

Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution

Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach is hard to hold its performance in practical scenarios when the degradation process is unknown. Despite existing blind SR methods proposed to solve this problem using blur kernel estimation, the perceptual quality and reconstruction accuracy are still unsatisfactory. In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model. We propose a components decomposition and co-optimization network (CDCN) for blind SR. Firstly, CDCN decomposes the input LR image into structure and detail components in feature space. Then, the mutual collaboration block (MCB) is presented to exploit the relationship between both two components. In this way, the detail component can provide informative features to enrich the structural context and the structure component can carry structural context for better detail revealing via a mutual complementary manner. After that, we present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process. Finally, a multi-scale fusion module followed by an upsampling layer is designed to fuse the structure and detail features and perform SR reconstruction. Empowered by such degradation-based components decomposition, collaboration, and mutual optimization, we can bridge the correlation between component learning and degradation modelling for blind SR, thereby producing SR results with more accurate textures. Extensive experiments on both synthetic SR datasets and real-world images show that the proposed method achieves the state-of-the-art performance compared to existing methods.

Optimal Wavelet Selection for Signal Denoising.

Wavelet denoising plays a key role in removing noise from signals and is widely used in many applications. In denoising, selection of the mother wavelet is desirable for maximizing the separation of noise and signal coefficients in the wavelet domain for effective noise thresholding. At present, wavelet selection is carried out in a heuristic manner or using a trial-and-error that is time consuming and prone to error, including human bias. This paper introduces a universal method to select optimal wavelets based on the sparsity of Detail components in the wavelet domain, an empirical approach. A mean of sparsity change ( ) parameter is defined that captures the mean variation of noisy Detail components. The efficacy of the presented method is tested on simulated and experimental signals from Electron Spin Resonance spectroscopy at various SNRs. The results reveal that the values of signal vary abruptly between wavelets, whereas for noise it displays similar values for all wavelets. For low Signal-to-Noise Ratio (SNR) data, the change in between highest and second highest value is ≈ 8 - 10% and for high SNR data it is around 5%. The mean of sparsity change increases with the SNR of the signal, which implies that multiple wavelets can be used for denoising a signal, whereas, the signal with low SNR can only be efficiently denoised with a few wavelets. Either a single wavelet or a collection of optimal wavelets (i.e., top five wavelets) should be selected from the highest values. The code is available on GitHub and the signalsciencelab.com website.

4-Senses compact exhibit: Novel tool to promote biodiversity

Natural history exhibits serve as a valuable means of raising public awareness and disseminating information about biodiversity. It is one of the ways to attract all generations to mainstream biodiversity by exploring those components in more depth and detail. This research focuses on developing a 4-senses compact exhibit as an innovative tool to promote biodiversity, engaging the senses of sight, hearing, smell, and touch. The study involves students from primary and secondary schools, as the exhibit is intended for various locations such as education and research centers, shopping centers, and schools. The research employs pre- and post-treatment questionnaires to assess the effectiveness of the 4-sense compact exhibit in promoting biodiversity. The results suggest this exhibit is a potent and novel tool for enhancing biodiversity awareness among school students, fostering a deeper understanding and appreciation for nature. Its potential as an educational resource for both primary and secondary schools make it a promising tool to instill a sense of care and appreciation for biodiversity from a young age, contributing to a more environmentally conscious future.

Wavelet-Based Dual-Task Network.

In image processing, wavelet transform (WT) offers multiscale image decomposition, generating a blend of low-resolution approximation images and high-resolution detail components. Drawing parallels to this concept, we view feature maps in convolutional neural networks (CNNs) as a similar mix, but uniquely within the channel domain. Inspired by multitask learning (MTL) principles, we propose a wavelet-based dual-task (WDT) framework. This novel framework employs WT in the channel domain to split a single task into two parallel tasks, thereby reforming traditional single-task CNNs into dynamic dual-task networks. Our WDT framework integrates seamlessly with various popular network architectures, enhancing their versatility and efficiency. It offers a more rational approach to resource allocation in CNNs, balancing between low-frequency and high-frequency information. Rigorous experiments on Cifar10, ImageNet, HMDB51, and UCF101 validate our approach's effectiveness. Results reveal significant improvements in the performance of traditional CNNs on classification tasks, and notably, these enhancements are achieved with fewer parameters and computations. In summary, our work presents a pioneering step toward redefining the performance and efficiency of CNN-based tasks through WT.

High-fidelity models for the online control of power ramps in fuel displacement systems – Revisiting the ISABELLE experiment in the OSIRIS Material Testing Reactor

The Jules Horowitz Reactor (JHR) is an MTR under construction at the CEA Cadarache, France. Its design and future operation build upon the lessons learned from the OSIRIS MTR. The CEA intends to transfer the knowledge accumulated with the ISABELLE1 loop of OSIRIS to the ADELINE loop of the JHR, both dedicated to power-ramp-type irradiation experiments, the purpose of which is to test the resistance of PWR fuel rod cladding under extreme levels of stress. In this article, we revisit the ETALISA experiment, a heat balance measurement experiment performed in 1992 for calibrating power ramps in ISABELLE1. We use high-fidelity modelling and simulation tools, especially the neutron-gamma TRIPOLI-4® Monte Carlo transport code, to calculate the detailed components of the heat balance, correction terms, and uncertainties. Comparisons between the simulations and the experiments show a very good agreement in the total linear heat generation rate of 400 W/cm at high power. The computed 2σ uncertainty is found to be 5%, a value essentially identical to the estimate derived in 1993 from an engineering approach. The use of modern simulation tools does not make it possible to improve upon this value, but provides a better understanding of the various components and corrections introduced in the total heat balance. The main limitations come from the ISABELLE1 online instrumentation, thermocouples and self-power neutron detectors, which set a limit on our very knowledge of the actual power ramp experimental conditions.

Optimized CNN‐Bi‐LSTM–Based BCI System for Imagined Speech Recognition Using FOA‐DWT

Speech imagery is emerging as a significant neuro‐paradigm for designing an electroencephalography (EEG)‐based brain–computer interface (BCI) system for the purpose of rehabilitation, medical neurology, and to aid people with disabilities in interacting with their surroundings. Neural correlates of speech imagery EEG signals are variable and weak as compared to the vocal state; hence, it is challenging to interpret them using machine learning (ML)–based classifiers. The applicability of modern deep learning methods such as convolutional neural networks (CNNs) and bidirectional long short‐term memory (Bi‐LSTM) networks has seen substantial advances in complex EEG signal analysis as compared to ML‐based methods. The objective of this article is to design a firefly‐optimized discrete wavelet transform (DWT) and CNN‐Bi‐LSTM–based imagined speech recognition (ISR) system to interpret imagined speech EEG signals. This study utilizes two publicly available datasets. EEG signal is enhanced using firefly optimization algorithm (FOA)–based optimized soft thresholding of high‐frequency detail components obtained by DWT decomposition. The enhanced EEG signal is augmented using sliding window data augmentation to increase the training data. Frequency‐domain features like power spectral density (PSD), frequency band power (FBP), band ratios, peak frequency, mean frequency, median frequency, spectral entropy, and relative power are extracted from augmented EEG segments. The extracted feature vector is fed to the designed CNN‐Bi‐LSTM classifier such that the EEG data are classified into two‐class, three‐class, and four‐class categories. To achieve optimal performance, the CNN‐Bi‐LSTM model was optimized using the Keras tuner library. The designed CNN consists of one‐dimensional (1‐D) convolutional layers and max pooling layers for familiarizing local associations along with mining hierarchical connections, and the Bi‐LSTM network acquires long‐term dependencies from the features learned by the former CNN. Bi‐LSTM network improves the performance and acquires potentially more affluent representations by looking at the sequence in both forward and reverse ways to capture representations that might be left unexploited by the sequential‐order kind alone. The performance of the designed FOA‐DWT‐CNN‐Bi‐LSTM–based ISR system is assessed using four evaluation measures: accuracy, F1 score, recall, and precision. It is found that the proposed system achieves the highest classification accuracy of 99.43 ± 2.5%, 94.41 ± 3.31%, and 89.57 ± 4.3% for two‐class, three‐class, and four‐class categories, respectively.

Development of Website-Based Accounting Information Systems of Comprehensive Income Reports for Citizen Water Management

The current management of community water businesses still uses a manual or conventional system. The conventional system that occurs is that the officer manually records the use of water made in a recap, and then the financial section has no accounting information system, so it still uses Google Spreadsheets and accounting processes that refer to the accounting standards designed. Based on existing problems, a website-based accounting information system for comprehensive income reports for citizen water management is needed. The research objective is to build a website-based Citizen Water Management Comprehensive Income Report Accounting Information System. The research method used was descriptive survey research, in which the author describes a situation or phenomenon systematically in a clear and detailed manner, including processes and components that occur, to find solutions to the problems found. The population in the study is the financial details of the water fund in 2022, and the sample is the financial details of the water report for October 2020. The types of data collection methods are interviews, observation, and library research. In research, the type of design used is descriptive analysis with primary data. One of the system methods is the waterfall method. The result of his research was the design of a comprehensive income reporting accounting information system for web-based community water management so that it facilitates the process of recording transactions and producing accurate financial reports for citizen water management.

Geoinformation modeling of city lawns

The article describes the possibility of using geoinformation modeling technology to create a three-dimensional model of a lawn area based on the formation of an accurate, detailed component geoinformation micro-model. The importance of lawn as an indicator of sustainable urban environment is described. The process of modeling on the basis of the obtained data set with the use of unmanned aerial vehicle and three-dimensional modeling environment Blender is revealed step by step. The possibilities of development of the technology of geoinformation modelling of lawns are pointed out, with a description of specific recommendations for improving the methodology proposed by the authors.

Stationary wavelet denoising of solid-state NMR spectra using multiple similar measurements

Accumulating several scans of free induction decays is always needed to improve the signal-to-noise ratio of NMR spectra, especially for the low gyromagnetic ratio solid-state NMR. In this study, we present a new denoising approach based on the correlations between multiple similar NMR spectra. Contrary to the simple averaging of multiple scans or denoising the final averaged spectrum, we propose a Wavelet-based Denoising technique for Multiple Similar scans(WDMS). Firstly, the stationary wavelet transform is applied to decompose every spectrum into approximation coefficients and detail coefficients. Then, the detail coefficients are multiplied by weights calculated based on Pearson's correlation coefficient and structural similarity index between approximation coefficients of different spectra. Finally, the average of these detailed components is used to denoise the spectra. The proposed method is carried on the assumption that noise between multiple spectra is uncorrelated while peak signal information is similar between different spectra, thus preserving the possibility of applying further processing to the data. As a demonstration, the standard wavelet denoise is applied to the WDMS-processed spectra, achieving a further increase in the S/N ratio. We confirm the reliability of the denoising approach based on multiple scans on 1D/2D solid-state MAS/static NMR spectra. In addition, we also show that this method can be used to deal with a single Car-Purcell-Meiboom-Gill (CPMG) echo train.

Dynamic safety management model for rail traffic control

In rail transport, increasing emphasis has been placed in recent years on improving safety levels. Therefore, more requirements and legal documents require risk analyses to be carried out at various stages of investment implementation. One of the leading legal documents that introduce the obligation to monitor risk is Directive (EU) 2016/798 of the European Parliament and of the Council of 11 May 2016 on railway safety and Commission Implementing Regulation (EU) No 402/2013 of 30 April 2013 on the common safety method for risk evaluation and assessment and re-pealing Regulation (EC) No 352/2009. Additionally, for traffic control systems, the requirements of CENELEC standards are mandatory. These documents present the subject of safety level and show its relation with the safety targets defined in the railway system, including the different ways of measuring them. Methods are also available to analyse the safety level of railway system components in detail, both at the level of individual components, subsystems, and the whole national railway system. However, after conducting an in-depth analysis of the literature, the authors of the article indicate that these methods are not consistent with each other. There is no method defined to present the direct relation of the safety level of the components of the system on the achievement of safety targets for the national railway system. The research and analysis aimed to define an approach, a method that would meet all legal requirements but at the same time would allow to clearly and reliably determine the safety level of the railway system. To define a unified approach, the authors of the article propose to develop a model of a dynamic object - a railway system safety model, which has also been verified on accurate safety data in rail transport in recent years. This model organises the process of safety management on railways and allows to determine values influencing the achievement of safety targets on an assumed level.

Impedance Variation of All-Solid-State Battery Cell Using Li10GeP2S12; States-of-Charge Dependence Visualized by Distribution-of-Relaxation Time Analysis

Electrochemical impedance spectroscopy (EIS) will support understanding of electrochemical phenomena in emerging energy devices, including all-solid-state Li ion batteries (ASSLiBs), only when the results are analyzed in a consistent way without arbitrariness. This study1 aimed to establish such a robust analysis for ASSLiB cells using a fast lithium ion conductor Li10 +x Ge1+x P2 −x S12 (LGPS)2, by applying distribution-of-relaxation time (DRT) analysis3. The DRT method distinctively visualized impedance changes depending on states of charge (SOCs). Pellet-type cells were prepared with an In–Li anode, LGPS separator, cathode composite comprising LGPS and LiNbO3-coated LiCoO2 powders4. Figure 1a shows the discharge curve that was recorded for the cell at the current density of 13.2 μA cm−2 (1/40 C-rate) and with the cut-off voltage of 1.9–3.6 V vs. Li+/In–Li (2.55–4.25 V vs. Li+/Li). The EIS measurements were performed during discharge process at various SOCs from 100% to 20%. Nyquist diagrams obtained from the EIS data are shown in Figure 1b, where two broad semicircles are observed at low and high frequency sides. This result indicates at least two electrochemical processes exist in the cell. Both semicircles become large in size upon decreasing SOCs from 100 % down to 20%, indicating that the cell has SOC-dependent electrochemical processes. Figure 1c shows DRT transformation of the EIS data, which helps to separate impedance components in detail. Here horizonal and vertical axes respectively represent the measurement frequency and polarization contribution at each frequency. The center frequency of each distinct peak corresponds to time constant [τ =1/(2πf)] for the respective electrochemical process, and thus the number of peaks matches that of electrochemical processes in the cell. Meanwhile, the peak area represents polarization contribution of each process. Consistently in the whole SOC range, the above DRT analysis indicates the semicircle at high frequency side is divided into two impedance contributions and that at low frequency side cannot be separated any more. This result is consistent with the DRT spectra from additionally prepared symmetric cells; in total three DRT peaks were observed with two peaks from interphase and interface between In–Li/LGPS, and one from LiNbO3-coated LiCoO2/LGPS interface. Accordingly, DRT diagram in Figure 1c was interpreted as indicated by arrows for each peak. This study demonstrated that DRT analysis supports a consistent interpretation of EIS data from ASSLiB cells at various SOCs and with different electrode configurations, and thus implied that the method will also help subsequent equivalent circuit model analysis for reproducible parametrization of electrochemical processes.1. S. Hori et al., “Understanding the impedance spectra of all-solid-state lithium battery cells with sulfide superionic conductors,” J. Power Sources 556, 232450 (2023).2. N. Kamaya et al., “A lithium superionic conductor,” Nat. Mater. 10, 682–686 (2011).3. S. Dierickx, A. Weber, E. Ivers-Tiffée, “How the distribution of relaxation times enhances complex equivalent circuit models for fuel cells,” Electrochim. Acta 355, 136764 (2020).4. N. Ohta et al., “LiNbO3-coated LiCoO2 as cathode material for all solid-state lithium secondary batteries,” Electrochem. Commun. 9, 1486-1490 (2007). Figure 1

Uniformity of Electrodeposited Coatings Onto Additively Manufactured Graphite Lattice Electrodes

The ability to uniformly electrodeposit metallic coatings onto geometrically complex substrates plays a significant role in developing specialized applications from coating detailed RF components to depositing catalytic coatings onto ordered foams electrodes for electrochemical flow reactors. However, there presently lacks guidelines for electrodeposition in porous media due to the problem’s multi-scale complexity. We present a framework for deconvoluting this process through continuum-level modeling and experimental validation of electrodeposited copper onto additively manufactured conductive lattice structures. Through numerical simulations we can predict thickness profiles and validate the model prediction and coating quality through micro-computed tomography. We further devise scaling laws with the aim of facilitating control over deposition uniformities on different lattice architectures and dimensions under fluid flow. In extension, the manufacturing platform for functionalized electrodes is extended to battery applications as use in novel electrode geometries within rechargeable secondary batteries as well as plating thickness evolution in custom redox flow battery cathodes for grid scale energy storage.This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 within the LDRD program 20-ERD-056. LLNL-ABS-847849.

Mass and efficiency optimization of a He-Xe Brayton cycle for mobile, land-based nuclear microreactors

Nuclear microreactors represent an option for distributed power generation, which can be deployed in military installations, remote off-grid villages, and locations recovering from natural disasters. Small system mass is necessary to achieve their mobility. In this study, a He-Xe gas-cooled reactor coupled with a Closed Brayton cycle (CBC) is optimized in terms of system mass and thermal efficiency. A single-channel model is developed for the reactor to predict the minimum mass according to the thermal–hydraulic limitations. Masses of the heat exchanger and turbo-machines are calculated based on empirical curves. The simple regenerative and intercooling cycles are considered. A parametric study is conducted to investigate the influence of key parameters on system mass and to explore the tradeoffs between different components that satisfy the power requirements of 8 MWe. Furthermore, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to optimize the system mass and thermal efficiency. Optimal values of critical parameters on the Pareto Frontier are presented and discussed. Results show that the masses of the reactor core and coolers dominate the total mass in most cases, and increasing the height of the reactor core can help reduce the system mass by allowing a smaller core radius. The Pareto Frontier shows that at thermal efficiencies around 40% or below, the system mass is depressed by using large CPR, molecular weight of the mixture, height of the reactor core, and small recuperator effectiveness. Increasing the recuperator's effectiveness from about 90% is required for higher thermal efficiency design, but its mass increases dramatically and can surpass the mass share of other components. The current analysis can provide a guide for detailed system and component design.

Design and Flight Qualification of a Flying Capacitor Multilevel Converter for Electric Aircraft Applications

Multilevel switched-capacitor converters, such as the flying capacitor multilevel (FCML) converter, offer high energy density and efficiency, over a wide voltage range, making them suitable for applications in electrified aircraft. However, concerns of reliability due to the increased number of active and passive components have limited the use of high-level count converters in aircraft applications. This work addresses the challenges of designing a high-level count converter with the goal of extremely high efficiency and power density, with maintained reliability and flight readiness. We present detailed component selection and design techniques to achieve this goal, along with a high-performance 28.2 kW/kg, 99.52% efficient high-voltage dc-dc step-up power converter for experimental validation. Finally, we demonstrate successful completion of critical flight qualification testing of the hardware prototype, including thermal, shock and vibration.